A No-reference Stereo Image Quality Evaluation Method

Abstract

The invention discloses a no-reference stereo image quality assessment method. The no-reference three-dimensional image quality assessment method comprises the following steps: in a training phase, obtaining classification labels of distorted stereo images by subjective experiments, constituting a training image set through all undistorted stereo images, all distorted stereo images and respective corresponding classification labels, and obtaining left and right viewpoint image feature dictionary tables and left and right viewpoint image quality dictionary tables of the training image set, and a transformation matrix by joint dictionary training, wherein the left and right viewpoint image feature dictionary tables and the left and right viewpoint image quality dictionary tables have discernibility; and in a test phase, obtaining a sparse coefficient matrix by optimization according to the left and right viewpoint image feature dictionary tables, and calculating a predicted value of objective assessment of image quality through the sparse coefficient matrix and the left and right viewpoint image quality dictionary tables. Since the left and right viewpoint image feature dictionary tables and the left and right viewpoint image quality dictionary tables have discernibility, the predicted value of objective assessment of image quality and a subjective assessment value keep relatively good consistency.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a no-reference stereoscopic image quality evaluation method. Background technique [0002] With the rapid development of image coding and display technologies, the research on image quality evaluation has become a very important link. The goal of the research on the objective evaluation method of image quality is to keep consistent with the subjective evaluation results as much as possible, so as to get rid of the time-consuming and boring subjective evaluation method of image quality, which can automatically evaluate the image quality by computer. According to the degree of reference and dependence on the original image, the objective image quality evaluation methods can be divided into three categories: full reference (Full Reference, FR) image quality evaluation methods, partial reference (Reduced Reference, RR) image quality evaluation methods and no reference ( No Refer...

AI Technical Summary

Problems solved by technology

At present, the existing no-reference image quality evaluation method uses machine learning to predict the evaluation model, but its computational complexity is high, and the training model needs to predict the subjective evaluation value of each evaluation image, which is not suitable for practical applications. There are certain limitations

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